Authors

Ahmed B Alarabi, Attayeb Mohsen, Kenji Mizuguchi, Fatima Z Alshbool, Fadi T Khasawneh

Journal

BMC Med Genomics 15, 83 (2022).

Abstract

Corona virus disease 2019 (COVID-19) increases the risk of cardiovascular occlusive/thrombotic events and is linked to poor outcomes. The underlying pathophysiological processes are complex, and remain poorly understood. To this end, platelets play important roles in regulating the cardiovascular system, including via contributions to coagulation and inflammation. There is ample evidence that circulating platelets are activated in COVID-19 patients, which is a primary driver of the observed thrombotic outcome. However, the comprehensive molecular basis of platelet activation in COVID-19 disease remains elusive, which warrants more investigation. Hence, we employed gene co-expression network analysis combined with pathways enrichment analysis to further investigate the aforementioned issues. Our study revealed three important gene clusters/modules that were closely related to COVID-19. These cluster of genes successfully identify COVID-19 cases, relative to healthy in a separate validation data set using machine learning, thereby validating our findings. Furthermore, enrichment analysis showed that these three modules were mostly related to platelet metabolism, protein translation, mitochondrial activity, and oxidative phosphorylation, as well as regulation of megakaryocyte differentiation, and apoptosis, suggesting a hyperactivation status of platelets in COVID-19. We identified the three hub genes from each of three key modules according to their intramodular connectivity value ranking, namely: COPE, CDC37, CAPNS1, AURKAIP1, LAMTOR2, GABARAP MT-ND1, MT-ND5, and MTRNR2L12. Collectively, our results offer a new and interesting insight into platelet involvement in COVID-19 disease at the molecular level, which might aid in defining new targets for treatment of COVID-19–induced thrombosis.

https://doi.org/10.1186/s12920-022-01222-y

Authors

Jonguk Park, Koji Hosomi, Hitoshi Kawashima, Yi-An Chen, Attayeb Mohsen, Harumi Ohno, Kana Konishi, Kumpei Tanisawa, Masako Kifushi, Masato Kogawa, Haruko Takeyama, Haruka Murakami, Tetsuya Kubota, Motohiko Miyachi, Jun Kunisawa, Kenji Mizuguchi

Journal

Nutrients, 14;10;2078. (2022)

Abstract

The gut microbiota is closely related to good health; thus, there have been extensive efforts dedicated to improving health by controlling the gut microbial environment. Probiotics and prebiotics are being developed to support a healthier intestinal environment. However, much work remains to be performed to provide effective solutions to overcome individual differences in the gut microbial community. This study examined the importance of nutrients, other than dietary fiber, on the survival of gut bacteria in high-health-conscious populations. We found that vitamin B1, which is an essential nutrient for humans, had a significant effect on the survival and competition of bacteria in the symbiotic gut microbiota. In particular, sufficient dietary vitamin B1 intake affects the relative abundance of Ruminococcaceae, and these bacteria have proven to require dietary vitamin B1 because they lack the de novo vitamin B1 synthetic pathway. Moreover, we demonstrated that vitamin B1 is involved in the production of butyrate, along with the amount of acetate in the intestinal environment. We established the causality of possible associations and obtained mechanical insight, through in vivo murine experiments and in silico pathway analyses. These findings serve as a reference to support the development of methods to establish optimal intestinal environment conditions for healthy lifestyles.

https://doi.org/10.3390/nu14102078

Authors

Attayeb Mohsen, Lokesh P. Tripathi, Kenji Mizuguchi

Journal

Front. Drug. Discov., 27 October 2021

Abstract

Machine learning techniques are being increasingly used in the analysis of clinical and omics data. This increase is primarily due to the advancements in Artificial intelligence (AI) and the build-up of health-related big data. In this paper we have aimed at estimating the likelihood of adverse drug reactions or events (ADRs) in the course of drug discovery using various machine learning methods. We have also described a novel machine learning-based framework for predicting the likelihood of ADRs. Our framework combines two distinct datasets, drug-induced gene expression profiles from Open TG–GATEs (Toxicogenomics Project–Genomics Assisted Toxicity Evaluation Systems) and ADR occurrence information from FAERS (FDA [Food and Drug Administration] Adverse Events Reporting System) database, and can be applied to many different ADRs. It incorporates data filtering and cleaning as well as feature selection and hyperparameters fine tuning. Using this framework with Deep Neural Networks (DNN), we built a total of 14 predictive models with a mean validation accuracy of 89.4%, indicating that our approach successfully and consistently predicted ADRs for a wide range of drugs. As case studies, we have investigated the performances of our prediction models in the context of Duodenal ulcer and Hepatitis fulminant, highlighting mechanistic insights into those ADRs. We have generated predictive models to help to assess the likelihood of ADRs in testing novel pharmaceutical compounds. We believe that our findings offer a promising approach for ADR prediction and will be useful for researchers in drug discovery.

https://doi.org/10.3389/fddsv.2021.768792

Authors

Jonguk Park, Kumiko Kato, Haruka Murakami, Koji Hosomi, Kumpei Tanisawa, Takashi Nakagata, Harumi Ohno, Kana Konishi, Hitoshi Kawashima, Yi-An Chen, Attayeb Mohsen, Jin-zhong Xiao, Toshitaka Odamaki, Jun Kunisawa, Kenji Mizuguchi, Motohiko Miyachi

Journal

BMC microbiology 21, Article number: 151 (2021).

Abstract

Background

Inter-individual variations in gut microbiota composition are observed even among healthy populations. The gut microbiota may exhibit a unique composition depending on the country of origin and race of individuals. To comprehensively understand the link between healthy gut microbiota and host state, it is beneficial to conduct large-scale cohort studies. The aim of the present study was to elucidate the integrated and non-redundant factors associated with gut microbiota composition within the Japanese population by 16S rRNA sequencing of fecal samples and questionnaire-based covariate analysis.

Results

A total of 1596 healthy Japanese individuals participated in this study via two independent cohorts, NIBIOHN cohort (n = 954) and MORINAGA cohort (n = 642). Gut microbiota composition was described and the interaction of these microorganisms with metadata parameters such as anthropometric measurements, bowel habits, medical history, and lifestyle were obtained. Thirteen genera, including Alistipes, Anaerostipes, Bacteroides, Bifidobacterium, Blautia, Eubacterium halli group, Faecalibacterium, Fusicatenibacter, Lachnoclostridium, Parabacteroides, Prevotella_9, Roseburia, and Subdoligranulum were predominant among the two cohorts. On the basis of univariate analysis for overall microbiome variation, 18 matching variables exhibited significant association in both cohorts. A stepwise redundancy analysis revealed that there were four common covariates, Bristol Stool Scale (BSS) scores, gender, age, and defecation frequency, displaying non-redundant association with gut microbial variance.

Conclusions

We conducted a comprehensive analysis of gut microbiota in healthy Japanese individuals, based on two independent cohorts, and obtained reliable evidence that questionnaire-based covariates such as frequency of bowel movement and specific dietary habit affects the microbial composition of the gut. To our knowledge, this was the first study to investigate integrated and non-redundant factors associated with gut microbiota among Japanese populations.

https://doi.org/10.1186/s12866-021-02215-0

Authors

Juneyoung Lee, Attayeb Mohsen, Anik Banerjee, Louise D. McCullough, Kenji Mizuguchi, Motomu Shimaoka, Hiroshi Kiyono, Eun J. Park.

Journal

Int. J. Mol. Sci. 22, no. 7: 3544. (2021)

Abstract

The intestinal epithelium serves as a dynamic barrier to protect the host tissue from exposure to a myriad of inflammatory stimuli in the luminal environment. Intestinal epithelial cells (IECs) encompass differentiated and specialized cell types that are equipped with regulatory genes, which allow for sensing of the luminal environment. Potential inflammatory cues can instruct IECs to undergo a diverse set of phenotypic alterations. Aging is a primary risk factor for a variety of diseases; it is now well-documented that aging itself reduces the barrier function and turnover of the intestinal epithelium, resulting in pathogen translocation and immune priming with increased systemic inflammation. In this study, we aimed to provide an effective epigenetic and regulatory outlook that examines age-associated alterations in the intestines through the profiling of microRNAs (miRNAs) on isolated mouse IECs. Our microarray analysis revealed that with aging, there is dysregulation of distinct clusters of miRNAs that was present to a greater degree in small IECs (22 miRNAs) compared to large IECs (three miRNAs). Further, miRNA–mRNA interaction network and pathway analyses indicated that aging differentially regulates key pathways between small IECs (e.g., toll-like receptor-related cascades) and large IECs (e.g., cell cycle, Notch signaling and small ubiquitin-related modifier pathway). Taken together, current findings suggest novel gene regulation pathways by epithelial miRNAs in aging within the gastrointestinal tissues

https://doi.org/10.3390/ijms22073544

Authors

Rutger A Vos, Toshiaki Katayama, Hiroyuki Mishima, Shin Kawano, Shuichi Kawashima, Jin-Dong Kim, Yuki Moriya, Toshiaki Tokimatsu, Atsuko Yamaguchi, Yasunori Yamamoto, Hongyan Wu, Peter Amstutz, Erick Antezana, Nobuyuki P Aoki, Kazuharu Arakawa, Jerven T Bolleman, Evan Bolton, Raoul JP Bonnal, Hidemasa Bono, Kees Burger, Hirokazu Chiba, Kevin B Cohen, Eric W Deutsch, Jesualdo T Fernández-Breis, Gang Fu, Takatomo Fujisawa, Atsushi Fukushima, Alexander García, Naohisa Goto, Tudor Groza, Colin Hercus, Robert Hoehndorf, Kotone Itaya, Nick Juty, Takeshi Kawashima, Jee-Hyub Kim, Akira R Kinjo, Masaaki Kotera, Kouji Kozaki, Sadahiro Kumagai, Tatsuya Kushida, Thomas Lütteke, Masaaki Matsubara, Joe Miyamoto, Attayeb Mohsen, Hiroshi Mori, Yuki Naito, Takeru Nakazato, Jeremy Nguyen-Xuan, Kozo Nishida, Naoki Nishida, Hiroyo Nishide, Soichi Ogishima, Tazro Ohta, Shujiro Okuda, Benedict Paten, Jean-Luc Perret, Philip Prathipati, Pjotr Prins, Núria Queralt-Rosinach, Daisuke Shinmachi, Shinya Suzuki, Tsuyosi Tabata, Terue Takatsuki, Kieron Taylor, Mark Thompson, Ikuo Uchiyama, Bruno Vieira, Chih-Hsuan Wei, Mark Wilkinson, Issaku Yamada, Ryota Yamanaka, Kazutoshi Yoshitake, Akiyasu C Yoshizawa, Michel Dumontier, Kenjiro Kosaki, Toshihisa Takagi

Journal

F1000Research, 2020;9:136.

Abstract

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

https://dx.doi.org/10.12688%2Ff1000research.18236.1

Authors

Yi-An Chen, Jonguk Park, Yayoi Natsume-Kitatani, Hitoshi Kawashima, Attayeb Mohsen, Koji Hosomi, Kumpei Tanisawa, Harumi Ohno, Kana Konishi, Haruka Murakami, Motohiko Miyachi, Jun Kunisawa, Kenji Mizuguchi

Journal

Plos one 2020, 15:12:e0243609

Abstract

With an ever-increasing interest in understanding the relationships between the microbiota and the host, more tools to map, analyze and interpret these relationships have been developed. Most of these tools, however, focus on taxonomic profiling and comparative analysis among groups, with very few analytical tools designed to correlate microbiota and the host phenotypic data. We have developed a software program for creating a web-based integrative database and analysis platform called MANTA (Microbiota And pheNoType correlation Analysis platform). In addition to storing the data, MANTA is equipped with an intuitive user interface that can be used to correlate the microbial composition with phenotypic parameters. Using a case study, we demonstrated that MANTA was able to quickly identify the significant correlations between microbial abundances and phenotypes that are supported by previous studies. Moreover, MANTA enabled the users to quick access locally stored data that can help interpret microbiota-phenotype relations. MANTA is available at https://mizuguchilab.org/manta/ for download and the source code can be found at https://github.com/chenyian-nibio/manta.

https://doi.org/10.1371/journal.pone.0243609

Authors

Attayeb Mohsen, Muftah Al-Mahdawi, Mostafa M Fouda, Mikihiko Oogane, Yasuo Ando, Zubair Md Fadlullah

Journal

ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6

Abstract

As we are about to embark upon the highly hyped “Society 5.0”, powered by the Internet of Things (IoT), traditional ways to monitor human heart signals for tracking cardio-vascular conditions are challenging, particularly in remote healthcare settings. On the merits of low power consumption, portability, and non-intrusiveness, there are no suitable IoT solutions that can provide information comparable to the conventional Electrocardiography (ECG). In this paper, we propose an IoT device utilizing a spintronic-technology-based ultra-sensitive Magnetic Tunnel Junction (MTJ) sensor that measures the magnetic fields produced by cardio-vascular electromagnetic activity, i.e. Magentocardiography (MCG). We treat the low-frequency noise generated by the sensor, which is also a challenge for most other sensors dealing with low-frequency bio-magnetic signals. Instead of relying on generic signal processing techniques such as moving average, we employ deep-learning training on bio-magnetic signals. Using an existing dataset of ECG records, MCG signals are synthesized. A unique deep learning model, composed of a one-dimensional convolution layer, Gated Recurrent Unit (GRU) layer, and a fully-connected neural layer, is trained using the labeled data moving through a striding window, which is able to smartly capture and eliminate the noise features. Simulation results are reported to evaluate the effectiveness of the proposed method that demonstrates encouraging performance.

https://doi.org/10.1109/ICC40277.2020.9148617

Authors

Attayeb Mohsen, Ahmed Alarabi

Journal

F1000Research vol. 9 452. 27 May. 2020

Abstract

https://doi.org/10.12688/f1000research.24156.1

Authors

Attayeb Mohsen, Jonguk Park, Yi-An Chen, Hitoshi Kawashima, Kenji Mizuguchi

Journal

BMC bioinformatics

Abstract

To increase the accuracy of microbiome data analysis, solving the technical limitations of the existing sequencing machines is required. Quality trimming is suggested to reduce the effect of the progressive decrease in sequencing quality with the increased length of the sequenced library. In this study, we examined the effect of the trimming thresholds (0–20 for QIIME1 and 0–30 for QIIME2) on the number of reads that remained after the quality control and chimera removal (the good reads). We also examined the distance of the analysis results to the gold standard using simulated samples. Quality trimming increased the number of good reads and abundance measurement accuracy in Illumina paired-end reads of the V3-V4 hypervariable region. Our results suggest that the pre-analysis trimming step should be included before the application of QIIME1 or QIIME2.

https://doi.org/10.12688/f1000research.24156.1

Authors

Asuka Kikuchi, Fairuz Binti Mohammadi Nasir, Akie Inami, Attayeb Mohsen, Shoichi Watanuki, Masayasu Miyake, Kazuko Takeda, Daigo Koike, Takayasu Ito, Junpei Sasakawa, Rin Matsuda, Kotaro Hiraoka, Marcus Maurer, Kazuhiko Yanai, Hiroshi Watabe, Manabu Tashiro

Journal

Human Psychopharmacology: Clinical and Experimental/Volume 33, Issue 2/e2655

Abstract

https://doi.org/10.1002/hup.2655

Authors

Tomomitsu Iida, Takeo Yoshikawa, Takuro Matsuzawa, Fumito Naganuma, Tadaho Nakamura, Yamato Miura, Attayeb S Mohsen, Ryuichi Harada, Ren Iwata, Kazuhiko Yanai

Journal

Glia, 63:7:123-1225

Abstract

Histamine is a physiological amine which initiates a multitude of physiological responses by binding to four known G‐protein coupled histamine receptor subtypes as follows: histamine H1 receptor (H1R), H2R, H3R, and H4R. Brain histamine elicits neuronal excitation and regulates a variety of physiological processes such as learning and memory, sleep–awake cycle and appetite regulation. Microglia, the resident macrophages in the brain, express histamine receptors; however, the effects of histamine on critical microglial functions such as chemotaxis, phagocytosis, and cytokine secretion have not been examined in primary cells. We demonstrated that mouse primary microglia express H2R, H3R, histidine decarboxylase, a histamine synthase, and histamine N‐methyltransferase, a histamine metabolizing enzyme. Both forskolin‐induced cAMP accumulation and ATP‐induced intracellular Ca2+ transients were reduced by the H3R agonist imetit but not the H2R agonist amthamine. H3R activation on two ubiquitous second messenger signalling pathways suggests that H3R can regulate various microglial functions. In fact, histamine and imetit dose‐dependently inhibited microglial chemotaxis, phagocytosis, and lipopolysaccharide (LPS)‐induced cytokine production. Furthermore, we confirmed that microglia produced histamine in the presence of LPS, suggesting that H3R activation regulate microglial function by autocrine and/or paracrine signalling. In conclusion, we demonstrate the involvement of histamine in primary microglial functions, providing the novel insight into physiological roles of brain histamine.

https://doi.org/10.1002/glia.22812

Authors

Takeo Yoshikawa, Tadaho Nakamura, Tetsuro Shibakusa, Mayu Sugita, Fumito Naganuma, Tomomitsu Iida, Yamato Miura, Attayeb Mohsen, Ryuichi Harada, Kazuhiko Yanai

Journal

The Journal of nutrition:144:10:1637-1641

Abstract

L-histidine is one of the essential amino acids for humans, and it plays a critical role as a component of proteins. L-histidine is also important as a precursor of histamine. Brain histamine is synthesized from L-histidine in the presence of histidine decarboxylase, which is expressed in histamine neurons. In the present study, we aimed to elucidate the importance of dietary L-histidine as a precursor of brain histamine and the histaminergic nervous system. C57BL/6J male mice at 8 wk of age were assigned to 2 different diets for at least 2 wk: the control (Con) diet (5.08 g L-histidine/kg diet) or the low L-histidine diet (LHD) (1.28 g L-histidine/kg diet). We measured the histamine concentration in the brain areas of Con diet–fed mice (Con group) and LHD-fed mice (LHD group). The histamine concentration was significantly lower in the LHD group [Con group vs. LHD group: histamine in cortex (means ± SEs): 13.9 ± 1.25 vs. 9.36 ± 0.549 ng/g tissue; P = 0.002]. Our in vivo microdialysis assays revealed that histamine release stimulated by high K+ from the hypothalamus in the LHD group was 60% of that in the Con group (P = 0.012). However, the concentrations of other monoamines and their metabolites were not changed by the LHD. The open-field tests showed that the LHD group spent a shorter amount of time in the central zone (87.6 ± 14.1 vs. 50.0 ± 6.03 s/10 min; P = 0.019), and the light/dark box tests demonstrated that the LHD group spent a shorter amount of time in the light box (198 ± 8.19 vs. 162 ± 14.1 s/10 min; P = 0.048), suggesting that the LHD induced anxiety-like behaviors. However, locomotor activity, memory functions, and social interaction did not differ between the 2 groups. The results of the present study demonstrated that insufficient intake of histidine reduced the brain histamine content, leading to anxiety-like behaviors in the mice.

https://doi.org/10.3945/jn.114.196105

Authors

Attayeb Mohsen, Takeo Yoshikawa, Yamato Miura, Tadaho Nakamura, Fumito Naganuma, Katsuhiko Shibuya, Tomomitsu Iida, Ryuichi Harada, Nobuyuki Okamura, Takehiko Watanabe, Kazuhiko Yanai

Journal

Neuropharmacology:81:188-194

Abstract

Histaminergic neurons are activated by histamine H3 receptor (H3R) antagonists, increasing histamine and other neurotransmitters in the brain. The prototype H3R antagonist thioperamide increases locomotor activity and anxiety-like behaviours; however, the mechanisms underlying these effects have not been fully elucidated. This study aimed to determine the mechanism underlying H3R-mediated behavioural changes using a specific H3R antagonist, JNJ-10181457 (JNJ).

First, we examined the effect of JNJ injection to mice on the concentrations of brain monoamines and their metabolites. JNJ exclusively increased Nτ-methylhistamine, the metabolite of brain histamine used as an indicator of histamine release, suggesting that JNJ dominantly stimulates the release of histamine release but not of other monoamines.

Next, we examined the mechanism underlying JNJ-induced behavioural changes using open-field tests and elevated zero maze tests. JNJ-induced increase in locomotor activity was inhibited by α-fluoromethyl histidine, an inhibitor of histamine synthesis, supporting that H3R exerted its effect through histamine neurotransmission. The JNJ-induced increase in locomotor activity in wild-type mice was preserved in H1R gene knockout mice but not in histamine H2 receptor (H2R) gene knockout mice. JNJ-induced anxiety-like behaviours were partially reduced by diphenhydramine, an H1R antagonist, and dominantly by zolantidine, an H2R antagonist. These results suggest that H3R blockade induces histamine release, activates H2R and elicits exploratory locomotor activity and anxiety-like behaviours.

https://doi.org/10.1016/j.neuropharm.2014.02.003